WEBVTT

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Every day for the last 3 years, we have

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seen dramatic images from the war
in Ukraine. Periodically, we also see how

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Ukraine is defending itself
by developing new tactics, but also new

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tools. Drones, in particular,
are frequently pointed out as the

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protagonist of the new warfare. We had a

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live demonstration of this technology at

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the Ukrainian pavilion during CES. The

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drones on display were very cheap
to produce in mass quantity but also very

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deadly. >> This is also
military drone and we are

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right now looking know how to use it
in a in a civil in the civil sphere.

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>> It's actually a kamicazi drone.

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>> Oh wow. So the idea
of this drone was to

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develop super super cheap and effective
comma because you need like enormous

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amount of them you know on the front line
because it flies only one way >> right

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>> and it's really really cheap and it can

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fly up to 100 km with 4 kilo of payload

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>> how cheap

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>> in Ukraine less than 200 $2,000 uh in

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Ukraine abroad it would be more

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expensive obviously because you know we

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have some like our country like help um

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helps companies like us in we're
not paying taxes and so on.

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>> Everybody will be a bit more expensive

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but still not 20,000 like
we see like for example

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>> in fact Eric Schmid is suggesting that

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every government should consider

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investing in alternative modern weapons

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like drones alongside the traditional

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war machines. Take a listen. [music]

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is take 10% and put it on new stuff.

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Spend 90% on all the stuff that you

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think you should buy and run a trial.

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Literally test whether that 10% is

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better than the 90%.
And for example, for every tank you buy,

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build a [clears throat] unmanned ground
vehicle. For every airplane you buy,

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build a thousand drones or whatever,

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whatever the numbers are appropriate and

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run the test. uh

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you should be skeptical in this in your
organization. If you don't have tech

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people like these people embedded in

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every layer of in in this case the
chancellory and the mods and so forth of

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the countries you're going to miss out

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on a once in aundundredyear transition.

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This transition is analogous to the
invention of the tank 100 years ago. And

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by the way, tanks are not a good place

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to be in right now. 2 kg drone costs

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what $5,000 takes out 5 million to30

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million in the case of US tanks. Boom.

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Just like that. And they're
very hard to defend against.

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>> Eric Schmidt was a guest during one of

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the satellite events of the Munich
Security Forum. And he has no doubt that

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what we are seeing in Ukraine has many
elements of the warfare of the future

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wherever this may happen. >> [music]
>> Um, I spent lots of time in Ukraine

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trying to understand the future of war
and I think I I understand what's going

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to happen. Um, and I'm not talking about
Ukraine specifically. I'm talking about

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combat. Um, all of us are familiar with

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tanks, boats, and air force jets of one

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kind or another. All of you command
some combination of them. In every case,

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there's going to be an automation
equivalent of that. Instead of buying

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tanks, you'll be buying unmanounded

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ground vehicles. And I'll describe why
in a sec. Instead of buying big ships,

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you'll be buying the equivalent of drone

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ships automated in various ways. And in

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terms of t and in terms of air force,
you'll have drones. And the drones will

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be attack drones, but the drones will

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also be able to defend themselves. This

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is the future of war. And of course the

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existing systems be around but the quicker
you can make this transition the

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smarter and more sophisticated your
national security approaches will be.

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The elements that are composed of this

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are first abundance. I come from a

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background of abundance. You all spend a

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time of scarcity and ex exquisite

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expensive things. Uh in my view that's
wrong. Sorry to be obnoxious but I'm an

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American. um it's much
better to say how do we

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solve this problem with abundance with

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inexpensive systems that work well in

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swarms and that technology now works. So

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the first thing is drive the prices

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down. And when I say down, I mean down

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by a factor of 10, not 10%. And you do

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that by using product lines of one kind

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or another. You use um consumer product

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lines, you use contract manufacturers.

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You don't use the traditional military
procurement. The second thing that you

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do

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is um think of it as autom automation,

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right? Autonomy. Um, so for example,

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with modern vision systems, you can have
automatic target detection, and so far

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the systems that I've watched in Ukraine
have had zero collateral damage. These

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things are accurate enough that they're
more accurate than humans, which is

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important in in com in combat and in

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national security. And the third one is

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AI dominance. And AI dominance can be

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interpreted as many things. It can be
interpreted as vision systems. What I'm

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talking about is in strategy and

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algorithmic warfare using all of these

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tools is the future. It's how war will
be done. And obviously having spent a

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fair amount of time watching this real

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war, these are to be avoided, right? So

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I'm not in any way endorsing this. But

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the the the core point I'm trying to say

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is that this is happening very very

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quickly in Ukraine and all of you will

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either help or be dragged into it or

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will fight it because you don't like it.

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But the fact of the matter is this is

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the future and I'm convinced of it. Wars

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are won and lost through a whole bunch

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of players. There's no single reason why

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you win or lose. But every successful

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war plan will fundamentally have AI
as its strategic base and as its targeting

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base. The only question is how much
scale do you have in order to do this?

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By the way, you have to have lots of
training data, right? So the key with AI

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based things is that you actually have
to the system has to learn. Um and thank

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goodness there aren't that many wars,
but things like how do you identify

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targets? What does the enemy do? How do
they respond? All of these are playing

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out right now between Russia and

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Ukraine. In AI development,

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the fastest gains right now are
occurring in coding and you see this

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with Cloud Code and Augment and

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Microsoft Copilot and so forth. Um, my

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category of jobs, the job that I did
50 years ago is going away. It's rather

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disturbing. Um, but the fact of the
matter is that's where all the initial

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gains are right now for a whole bunch of

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reasons. If you can write code, you can
also write cyber attacks. So, one of the

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things that's going to happen is you're
gonna have an enormous number of zeroday

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cyber attacks coming out of this because

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of this innovation cycle. So, having

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said that, AI and AI servers that are
used in the military are legitimate

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military targets and therefore need
to be hardened and protected. They need to

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be hardened physically. They need to have
reliable power. But also the models

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uh there are things called adversarial
attacks where you can go in and take the

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model which is from your perspective
is just a bunch of numbers and change them

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enough that the AI does the wrong thing.

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So protecting them cyber security cyber
attacks are all incredibly important. I

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think this audience knows this is a

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growth business. Unfortunately when I

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talk about AI globally I always talk

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about what the US is doing and what

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China is doing. And the simple summary
is the US, thank God that the American

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financial system will give these people
billions and billions of dollars with no

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credible strategy for for making money

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at it. Um, Germans don't do that.

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Europeans don't do that. Um, but the

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fact of the matter is this enormous wave.
It's $2 trillion of capital going

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into the data centers in America and
around the world for those companies. Um

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the estimate for power needed in the

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United States is 90 gawatts
of additional power and as reference if

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you're not familiar with this the nuclear
power plant is around 1.5 gawatt

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gives you a sense of the scale involved
and this is these are the plans in China

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you have the same thing except that
the Chinese are using a technique called

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open source they're releasing all of the
weights and models um which is important

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strategically because it means the
majority of the world the third world if

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you will the other countries that we
don't typically visit are going to be

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using Chinese models not American models

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whereas Europe Europe has a couple of

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leading champions Mstral is one I'm an
investor for example but Europe does not

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have the scale does not have the data
centers does not have the financing to

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do it this is a tragedy Europe should

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organize in my view and I've said this

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and I'll say it again your own

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open-source models and they should be

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done thank you Um, it makes no sense for

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you to to allow your intelligence
systems and I mean human intelligence,

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not military intelligence, to be

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controlled by the US or China, right?

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Europe has its own special thing going
and I happen to like Europe and I want

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you guys to be successful and I don't
want you to be overrun by the Chinese or

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the US. You're going to have to do this
and furthermore it's going to cost you

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10 to20 billion.

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That's what they cost.

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Why open source? >> Um,
open source is the quickest way to

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catch up

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and having hired tens of thousands of

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people in Europe, the engineers
in Europe are fantastic, right? So, you

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need a way of organizing them. You need
a way to move quickly and open source is

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the easiest way to do it. My specific
proposal is that Brussels or equivalent

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organize a group of 10 computer
scientists who are obviously European

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and align with the British because
they're kind of near you um and figure

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it out. It can be done fairly quickly.

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>> The importance of open-source models is
often mentioned in this type of meetings

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also from the leading authorities
in this field. Take a listen. Let's face

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it, the single most important thing that
has happened last year, if you were to

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ask me the one single most important

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event last year and how it has activated

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AI researchers here in this region, it's

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actually Llama 2. It's an open- source

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model. All were possible because of some

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of these open source languages. And so I

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think that democratizing

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activating every region, activating

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every country to join the AI advance is

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probably one of the most important thing.

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>> For more content about the impact
of the Yama model and the importance of

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opensource models in general, you can
watch the video at the link above. But

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let's carry on with the future of warfare.

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What I'm going to say is that in a

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national security perspective,

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first the money is showing up which is

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great and that money needs to be applied

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to the right areas specifically the

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algorithms lowcost a factors technically

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drones drone industry you have a number
of national champions here you have a in

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Germany you have a company in Helsing
called Helsing that's quite good um

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there are a number of such examples and

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we need a lot more of them the um I most

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interested in not the problem of volume
which everyone is focused on because I

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think the volume stuff will
ultimately be sorted out but rather the

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intelligence layer and I'll give you an

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example

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um the way AI is evolving is we start

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with um the AI that you all know which

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is sort of chat GPT language models
things like that and then we move to

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strategy and planning which is where we
are now with strategy and planning you

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can begin to do steps Think of it as

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workflow. And when you can do that,
this is what militaries do. This is what

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businesses do. This is what universities
do. Europe should lead in the agentic

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revolution where you build agents
to solve the problems that your citizens,

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your military, your universities have.

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This is the year of the agentic
revolution. And that stuff is happening

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very fast. I'm talking about every month.
You saw last week all the

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publicity about a fake world of agents
that were beginning to learn how to

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interact with each other. And everyone
was surprised that they didn't

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immediately try to assassinate each other.
Well, that's presumably in game

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three of the simulation. But the

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important point is that the agent model

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is going to drive huge revenue in

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America. It should drive huge revenue in

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Europe. Along with that, start thinking

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about military uses. There's all
the supply chain stuff that's it. And you

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all spend so much money in my view
wasted it on these complicated systems

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just to have agents that figure
this stuff out and move it around. It's

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possible now using a technique called
MCP which is used throughout the

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industry model context protocol.
You basically take all your databases and

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you start asking it questions and it
figures it out. I mean these are huge

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breakthroughs architecturally and Europe

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is not leading in these areas. They will

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make you much more effective in whatever

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your objective is military, business,

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profit, entertainment, education. The

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the key next step after that is what

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agents do you build for war. So most

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people when they talk about AI talk
about it in the context of what in

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America is called project maven, which
is essentially vision and the project

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maven thing was very controversial.

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Google started it with the Pentagon.

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Google canceled it. Um, I was on the
Pentagon side, which is a mess. Um, and

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then eventually it's been taken
over largely by a palunteer.

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That's not the kind of AI I'm talking

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about. It's an enabler for reasoning.

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And every general I've met talks
about how they want to have battlefield

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awareness and they want to be able
to see everything. Well, here's what I

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want. When I sit in a command center and

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I watch the commanders who are very very

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committed to doing the right thing, I

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want the computer to give them a plan, a

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battle plan that we can prove will win

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in a series of iterations. There's
a technique called reinforcement learning

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which was invented um some years ago.
It was heavily used by Deep Mind winning

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the go game if you remember that about
10 years ago. And reinforcement learning

00:14:39.760 --> 00:14:44.317
allows you to sort of prove what the
optimal paths are. So here's an example.

00:14:44.400 --> 00:14:47.997
This would never happen, but you
and I are opponents. You have you have

00:14:48.080 --> 00:14:50.317
algorithms and I have algorithms. So now

00:14:50.400 --> 00:14:53.197
you and you and I are going to have a um

00:14:53.280 --> 00:14:56.477
and we're we're mad at each other. What

00:14:56.560 --> 00:14:59.277
is the deterrence that keeps you and me

00:14:59.360 --> 00:15:01.997
from fighting each other? Unlike

00:15:02.080 --> 00:15:06.797
traditional weapons,
you can't count the algorithms.

00:15:06.880 --> 00:15:08.637
If your algorithms are better than mine,

00:15:08.720 --> 00:15:11.357
and I suspect they would be, I better be

00:15:11.440 --> 00:15:14.157
very careful before I do something

00:15:14.240 --> 00:15:16.237
because you get a vote and you get a

00:15:16.320 --> 00:15:19.837
vote to to win. I spent a lot of time

00:15:19.920 --> 00:15:21.997
trying to figure out what this fight

00:15:22.080 --> 00:15:26.157
looks like. I'm sure within two or three

00:15:26.240 --> 00:15:29.517
years, algorithmic warfare as a category

00:15:29.600 --> 00:15:34.637
will exist. What I don't believe is
that the algorithms will be the same. And

00:15:34.720 --> 00:15:38.157
I'll give you an example.
In one of the more recent battles in

00:15:38.240 --> 00:15:42.557
Ukraine, and I won't go into the details,
um it was pretty clear that the

00:15:42.640 --> 00:15:47.437
Russians were focused on land and the
Ukrainians were focused on the people on

00:15:47.520 --> 00:15:48.877
the other side, right? These are

00:15:48.960 --> 00:15:52.157
different military strategies.

00:15:52.240 --> 00:15:55.197
It's not possible to model strategies

00:15:55.280 --> 00:15:58.077
where you have different goals, right?

00:15:58.160 --> 00:16:01.997
It's too hard. So the deterrence is if

00:16:02.080 --> 00:16:06.717
you and I have different strategies,
I don't know what you're really going

00:16:06.800 --> 00:16:11.517
after and therefore I can't model what
you're going to do. You become harder to

00:16:11.600 --> 00:16:15.597
predict with less
prediction capability. I'm

00:16:15.680 --> 00:16:18.957
less likely to start a war. So deterrence,

00:16:19.040 --> 00:16:22.317
>> it is a form of deterrence. And and the

00:16:22.400 --> 00:16:24.237
reason I'm hammering on this is that

00:16:24.320 --> 00:16:26.317
almost everybody learned from the

00:16:26.400 --> 00:16:29.997
nuclear stuff. Sorry to as an aside. So

00:16:30.080 --> 00:16:31.757
Henry Ken and I were best best friends.

00:16:31.840 --> 00:16:36.557
We wrote two books together and uh
and of course I miss him every day and he

00:16:36.640 --> 00:16:40.317
spent a lot of time here. He was by the
way here at the original MSSE way back

00:16:40.400 --> 00:16:42.877
when when I think none of us were here.

00:16:42.960 --> 00:16:45.437
Um and but anyway, Henry would would

00:16:45.520 --> 00:16:50.077
tell all these stories about how he did
deterrence and how the deterrence in the

00:16:50.160 --> 00:16:55.197
1950s was erected and it was fundamentally
because you could count things.

00:16:55.280 --> 00:16:59.117
I'm talking about a world where you
don't know. You can't model what the

00:16:59.200 --> 00:17:01.197
algorithms are going to do. Algorithmic

00:17:01.280 --> 00:17:03.997
warfare should be extremely

00:17:04.080 --> 00:17:08.557
self-deterring because of that issue.

00:17:08.640 --> 00:17:13.117
>> Some of these early capabilities
were already on display at the Ukrainian

00:17:13.200 --> 00:17:15.437
booth during CES.

00:17:15.520 --> 00:17:17.997
>> Run uh you know
the sophisticated things

00:17:18.080 --> 00:17:21.677
like like uh AI models. Then you just

00:17:21.760 --> 00:17:26.317
fly over your fields and the system
automatically makes a photos detect what

00:17:26.400 --> 00:17:29.917
what are you going you know to detect
and then just collects all the

00:17:30.000 --> 00:17:33.597
statistics calculates all the statistics
that you want and it depicts here on

00:17:33.680 --> 00:17:35.997
your on your screen.
>> There's wishful thinking and then

00:17:36.080 --> 00:17:39.997
there's reality. The wishful thinking is
that there's going to be a new algorithm

00:17:40.080 --> 00:17:41.597
that replaces the transformer

00:17:41.680 --> 00:17:43.197
architecture that Google invented in

00:17:43.280 --> 00:17:47.517
2017 that uses less hardware. That is a

00:17:47.600 --> 00:17:50.717
great idea. It's it's not obvious how

00:17:50.800 --> 00:17:53.677
and when that will occur. If you look at

00:17:53.760 --> 00:17:55.837
let's use Google as an example. Google

00:17:55.920 --> 00:17:58.557
is a highly efficient answering machine.

00:17:58.640 --> 00:18:03.437
The computational costs of doing the same
thing with any large language model

00:18:03.520 --> 00:18:07.437
are many orders of magnitude more
expensive which is why these things take

00:18:07.520 --> 00:18:11.837
so much time and the value is there.
And the part of the reason is that they do

00:18:11.920 --> 00:18:15.677
something called test time planning
where they are actually learning as

00:18:15.760 --> 00:18:20.637
they're going. They literally you you
you feed it what it knows and it knows

00:18:20.720 --> 00:18:24.077
something and then you feed it back
in again. It's called it's essentially a

00:18:24.160 --> 00:18:26.237
recursive process. The important thing

00:18:26.320 --> 00:18:28.877
about all of this is it uses an enormous

00:18:28.960 --> 00:18:31.277
amount of energy. Um as I mentioned

00:18:31.360 --> 00:18:35.517
before in the United States the um it's

00:18:35.600 --> 00:18:38.877
pretty clear that every real estate
developer in the United States has

00:18:38.960 --> 00:18:42.477
become obsessed with making more
money from data center builds than from

00:18:42.560 --> 00:18:44.877
building building on farmland. And the

00:18:44.960 --> 00:18:47.757
US has lots of extra land. So you're

00:18:47.840 --> 00:18:52.637
going to see lots and lots of that. And
this particular company um is working in

00:18:52.720 --> 00:18:54.637
an area called the Perian Basin which

00:18:54.720 --> 00:18:57.037
has a great deal of natural gas. In

00:18:57.120 --> 00:18:59.677
looking at it for Europe, Europe has uh

00:18:59.760 --> 00:19:04.957
France has excess nuclear capacity
as far as I can tell and the the only

00:19:05.040 --> 00:19:08.717
problem is the prices in France
for energy are high. I was recently

00:19:08.800 --> 00:19:12.077
contacted by somebody who's figured out
a way to build a gigawatt in Bulgaria

00:19:12.160 --> 00:19:16.397
which I thought was interesting.
My guess for Europeans because the energy

00:19:16.480 --> 00:19:18.557
costs are so high is that people are

00:19:18.640 --> 00:19:20.397
here. The training will be done

00:19:20.480 --> 00:19:23.757
somewhere else. This is a a national

00:19:23.840 --> 00:19:27.837
security issue for Europe as a strategic

00:19:27.920 --> 00:19:30.637
issue. If I were doing things for the

00:19:30.720 --> 00:19:33.597
military in Europe, I'd want my training

00:19:33.680 --> 00:19:36.157
to be done in a European NATO type

00:19:36.240 --> 00:19:38.877
country. European values, European laws,

00:19:38.960 --> 00:19:43.917
European privacy laws. And that's hence
why I keep going on about Europe needs a

00:19:44.000 --> 00:19:47.517
strategy. You need some form of open
source model and you need to do the

00:19:47.600 --> 00:19:51.277
training in Europe.
>> So you're building this facility where

00:19:51.360 --> 00:19:55.837
natural gas is abundant. So you're in
the right place for data centers. Middle

00:19:55.920 --> 00:19:59.277
East would seem to be a logical place
to put some of them, but not the ones for

00:19:59.360 --> 00:20:03.117
military use. >> Yeah,
I think I think that for the

00:20:03.200 --> 00:20:06.797
militaries will ultimately figure out
that controlling that value chain is

00:20:06.880 --> 00:20:11.437
really important. You want to have pretty
much vertical control over your stuff.

00:20:11.520 --> 00:20:13.517
>> Now before this event, we followed the

00:20:13.600 --> 00:20:16.397
World Economic Forum in Davos and it was

00:20:16.480 --> 00:20:21.117
evident that even among the leaders in AI,
there was no consensus about the

00:20:21.200 --> 00:20:23.357
timeline [music] for AI to become a

00:20:23.440 --> 00:20:25.917
truly revolutionary. Eric Schmidt

00:20:26.000 --> 00:20:29.517
confirms the current situation.

00:20:29.600 --> 00:20:33.277
The real narrative is that America

00:20:33.360 --> 00:20:37.197
because of money, capital scale,

00:20:37.280 --> 00:20:39.997
craziness, people like me touting a

00:20:40.080 --> 00:20:41.517
future in two years where everything

00:20:41.600 --> 00:20:44.477
will be different. The AI boom in the US

00:20:44.560 --> 00:20:48.077
is like a religion, right? And I call it

00:20:48.160 --> 00:20:52.317
the San Francisco consensus. And um the
people in San Francisco that I know and

00:20:52.400 --> 00:20:54.317
I spent lots of time with them all

00:20:54.400 --> 00:20:57.837
believe within 2 or 3 years

00:20:57.920 --> 00:20:59.437
something very interesting is going to

00:20:59.520 --> 00:21:03.597
happen. And one of my friends said h and

00:21:03.680 --> 00:21:06.477
he goes then we don't know what will

00:21:06.560 --> 00:21:09.437
happen. And by the way this is the way

00:21:09.520 --> 00:21:12.557
the pitch goes. When these systems these

00:21:12.640 --> 00:21:14.317
systems are smart and they can do

00:21:14.400 --> 00:21:17.437
planning and so forth, there's a point

00:21:17.520 --> 00:21:22.237
at which the the system
can begin to build itself.

00:21:22.320 --> 00:21:24.397
It can take off on its own. The

00:21:24.480 --> 00:21:27.277
technical term is called rein uh re

00:21:27.360 --> 00:21:32.157
essentially recursive self-improvement
recursively getting smarter and smarter.

00:21:32.240 --> 00:21:35.757
Now, if you think about it,
if the computer evolves faster than we do,

00:21:35.840 --> 00:21:40.317
we're in trouble. And then everyone goes,
"Oh my god, we're in trouble." Now

00:21:40.400 --> 00:21:41.997
I personally don't think it'll be within

00:21:42.080 --> 00:21:45.197
2 years but the the but I do believe

00:21:45.280 --> 00:21:48.077
that over a 5 or 10 year period the rise

00:21:48.160 --> 00:21:51.117
of this foreign intelligence and how it

00:21:51.200 --> 00:21:54.557
gets hard harnessed is going to be huge.

00:21:54.640 --> 00:21:57.837
My view about Europe since we're
in Europe is Europe needs to be a player

00:21:57.920 --> 00:22:01.917
there and I don't see Europe doing
it right now because of the lack of

00:22:02.000 --> 00:22:04.637
hardware and companies and so forth and

00:22:04.720 --> 00:22:06.957
uh Europe is in big danger to be left

00:22:07.040 --> 00:22:10.637
out of this and China for example does

00:22:10.720 --> 00:22:14.557
understand this and the Chinese are
working very very hard using a different

00:22:14.640 --> 00:22:19.277
approach. They do more computation
at the edge. The computers are not as

00:22:19.360 --> 00:22:23.677
powerful because the US won't allow
them to get those chips. Um, and that's

00:22:23.760 --> 00:22:28.397
there's a complicated discussion about
all of that. I should mention that ASML,

00:22:28.480 --> 00:22:32.077
which is a European company,
is incredibly impressive and incredibly

00:22:32.160 --> 00:22:36.237
important. If Europe could just create
more ASMLs, everyone would be good,

00:22:36.320 --> 00:22:39.037
right? It's it's a true monopoly on a

00:22:39.120 --> 00:22:41.437
technology that is essential for the AI

00:22:41.520 --> 00:22:43.837
revolution. The these transitions take

00:22:43.920 --> 00:22:46.717
decades, right? Did anyone here arrive

00:22:46.800 --> 00:22:50.557
in a driverless car here in Munich?

00:22:50.640 --> 00:22:55.837
This is the car capital of the world,
isn't it? Germany, right? By the way, in

00:22:55.920 --> 00:22:58.957
America, most people don't arrive
in self-driving cars, too. And they were

00:22:59.040 --> 00:23:02.237
invented in the basically the late '9s.

00:23:02.320 --> 00:23:05.117
So, what is that 30 years, right? It

00:23:05.200 --> 00:23:07.277
just takes a long time. The industry

00:23:07.360 --> 00:23:10.237
uses the phrase technology overhang to

00:23:10.320 --> 00:23:12.317
refer to the fact that the rate of

00:23:12.400 --> 00:23:15.197
diffusion or adoption of the techn tech

00:23:15.280 --> 00:23:18.077
the stuff we're talking about is slow.

00:23:18.160 --> 00:23:21.917
It's slow for many reasons. Sometimes
it doesn't work. Sometimes you don't have

00:23:22.000 --> 00:23:25.437
the right people on the other side.
But at least in the case of national

00:23:25.520 --> 00:23:27.757
security, you have an obligation to

00:23:27.840 --> 00:23:30.077
protect your countries. You have to, in

00:23:30.160 --> 00:23:33.437
my view, take the jump, take the risk,

00:23:33.520 --> 00:23:38.077
and at least run the test. Right? To me,
it's an it's a moral imperative. It's a

00:23:38.160 --> 00:23:43.277
national imperative. And is exemplified
in the war. um with respect to to the

00:23:43.360 --> 00:23:47.197
rate at which this stuff is going
to happen, a lot of it depends on how easy

00:23:47.280 --> 00:23:51.277
it will be for normal people to use it,
right? So, for example, Google and I

00:23:51.360 --> 00:23:55.997
obviously have a pro Google bias.
Um many of its Gemini answers are really

00:23:56.080 --> 00:23:57.677
better than the search results, right?

00:23:57.760 --> 00:24:01.277
And Gemini is really good.
It's currently the best performing model,

00:24:01.360 --> 00:24:03.117
right? So, it's according to the

00:24:03.200 --> 00:24:06.637
rankings. So, good for them. Uh so the

00:24:06.720 --> 00:24:10.397
the key thing to understand here
is you have this brutal competition for

00:24:10.480 --> 00:24:14.077
innovation which is being funded
by trillions of dollars. We all benefit

00:24:14.160 --> 00:24:18.157
from that competition. Let's see how far
it goes. Specific things that are going

00:24:18.240 --> 00:24:21.037
to happen are agents will be used by

00:24:21.120 --> 00:24:24.077
businesses to improve customer service

00:24:24.160 --> 00:24:28.477
in there's evidence that customer
service jobs for example are declining

00:24:28.560 --> 00:24:32.637
because you can automate those.
A general rule about automation is that

00:24:32.720 --> 00:24:37.277
highly um highly
repetitive jobs and highly

00:24:37.360 --> 00:24:39.117
dangerous jobs are automated out first.

00:24:39.200 --> 00:24:40.237
And I think that's what you'll see.

00:24:40.320 --> 00:24:42.340
[music]
